{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "from plotly_resampler import unregister_plotly_resampler\n",
    "\n",
    "from neuralprophet import NeuralProphet, set_random_seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Important to set seed for reproducibility\n",
    "set_random_seed(42)\n",
    "\n",
    "\n",
    "def get_system_speed():\n",
    "    repeats = 5\n",
    "    benchmarks = np.array([])\n",
    "    for a in range(0, repeats):\n",
    "        start = time.time()\n",
    "        for i in range(0, 1000):\n",
    "            for x in range(1, 1000):\n",
    "                3.141592 * 2**x\n",
    "            for x in range(1, 1000):\n",
    "                float(x) / 3.141592\n",
    "            for x in range(1, 1000):\n",
    "                float(3.141592) / x\n",
    "\n",
    "        end = time.time()\n",
    "        duration = end - start\n",
    "        duration = round(duration, 3)\n",
    "        benchmarks = np.append(benchmarks, duration)\n",
    "\n",
    "    print(f\"System speed: {round(np.mean(benchmarks), 5)}s\")\n",
    "    print(f\"Standart deviation: {round(np.std(benchmarks), 5)}s\")\n",
    "    return benchmarks.mean(), benchmarks.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_metrics_plot(metrics):\n",
    "    # Deactivate the resampler since it is not compatible with kaleido (image export)\n",
    "    unregister_plotly_resampler()\n",
    "\n",
    "    # Plotly params\n",
    "    prediction_color = \"#2d92ff\"\n",
    "    actual_color = \"black\"\n",
    "    line_width = 2\n",
    "    xaxis_args = {\"showline\": True, \"mirror\": True, \"linewidth\": 1.5, \"showgrid\": False}\n",
    "    yaxis_args = {\n",
    "        \"showline\": True,\n",
    "        \"mirror\": True,\n",
    "        \"linewidth\": 1.5,\n",
    "        \"showgrid\": False,\n",
    "        \"rangemode\": \"tozero\",\n",
    "        \"type\": \"log\",\n",
    "    }\n",
    "    layout_args = {\n",
    "        \"autosize\": True,\n",
    "        \"template\": \"plotly_white\",\n",
    "        \"margin\": go.layout.Margin(l=0, r=10, b=0, t=30, pad=0),\n",
    "        \"font\": dict(size=10),\n",
    "        \"title\": dict(font=dict(size=10)),\n",
    "        \"width\": 1000,\n",
    "        \"height\": 200,\n",
    "    }\n",
    "\n",
    "    metric_cols = [col for col in metrics.columns if not (\"_val\" in col or col == \"RegLoss\" or col == \"epoch\")]\n",
    "    fig = make_subplots(rows=1, cols=len(metric_cols), subplot_titles=metric_cols)\n",
    "    for i, metric in enumerate(metric_cols):\n",
    "        fig.add_trace(\n",
    "            go.Scatter(\n",
    "                y=metrics[metric],\n",
    "                name=metric,\n",
    "                mode=\"lines\",\n",
    "                line=dict(color=prediction_color, width=line_width),\n",
    "                legendgroup=metric,\n",
    "            ),\n",
    "            row=1,\n",
    "            col=i + 1,\n",
    "        )\n",
    "        if f\"{metric}_val\" in metrics.columns:\n",
    "            fig.add_trace(\n",
    "                go.Scatter(\n",
    "                    y=metrics[f\"{metric}_val\"],\n",
    "                    name=f\"{metric}_val\",\n",
    "                    mode=\"lines\",\n",
    "                    line=dict(color=actual_color, width=line_width),\n",
    "                    legendgroup=metric,\n",
    "                ),\n",
    "                row=1,\n",
    "                col=i + 1,\n",
    "            )\n",
    "        if metric == \"Loss\":\n",
    "            fig.add_trace(\n",
    "                go.Scatter(\n",
    "                    y=metrics[\"RegLoss\"],\n",
    "                    name=\"RegLoss\",\n",
    "                    mode=\"lines\",\n",
    "                    line=dict(color=actual_color, width=line_width),\n",
    "                    legendgroup=metric,\n",
    "                ),\n",
    "                row=1,\n",
    "                col=i + 1,\n",
    "            )\n",
    "    fig.update_xaxes(xaxis_args)\n",
    "    fig.update_yaxes(yaxis_args)\n",
    "    fig.update_layout(layout_args)\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "DIR = \"~/github/neural_prophet\"\n",
    "DATA_DIR = os.path.join(DIR, \"tests\", \"test-data\")\n",
    "PEYTON_FILE = os.path.join(DATA_DIR, \"wp_log_peyton_manning.csv\")\n",
    "AIR_FILE = os.path.join(DATA_DIR, \"air_passengers.csv\")\n",
    "YOS_FILE = os.path.join(DATA_DIR, \"yosemite_temps.csv\")\n",
    "ENERGY_PRICE_DAILY_FILE = os.path.join(DATA_DIR, \"tutorial04_kaggle_energy_daily_temperature.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(YOS_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = NeuralProphet(\n",
    "    n_lags=36,\n",
    "    n_forecasts=12,\n",
    "    changepoints_range=0.9,\n",
    "    n_changepoints=30,\n",
    "    weekly_seasonality=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency 5T corresponds to 99.995% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - 5min\n",
      "WARNING - (NP.data.processing._handle_missing_data) - 12 missing values in column y were detected in total. \n",
      "INFO - (NP.data.processing._handle_missing_data) - 12 NaN values in column y were auto-imputed.\n",
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n",
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n",
      "WARNING - (NP.forecaster.fit) - When Global modeling with local normalization, metrics are displayed in normalized scale.\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency 5T corresponds to 99.994% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - 5min\n",
      "INFO - (NP.config.init_data_params) - Setting normalization to global as only one dataframe provided for training.\n",
      "INFO - (NP.utils.set_auto_seasonalities) - Disabling yearly seasonality. Run NeuralProphet with yearly_seasonality=True to override this.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System speed: 0.4284s\n",
      "Standart deviation: 0.00712s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.config.set_auto_batch_epoch) - Auto-set batch_size to 128\n",
      "INFO - (NP.config.set_auto_batch_epoch) - Auto-set epochs to 90\n",
      "WARNING - (NP.config.set_lr_finder_args) - Learning rate finder: The number of batches (132) is too small than the required number                     for the learning rate finder (255). The results might not be optimal.\n"
     ]
    },
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       "Finding best initial lr:   0%|          | 0/255 [00:00<?, ?it/s]"
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   "source": [
    "df_train, df_test = m.split_df(df=df, freq=\"5min\", valid_p=0.1)\n",
    "\n",
    "system_speed, std = get_system_speed()\n",
    "start = time.time()\n",
    "\n",
    "metrics = m.fit(df_train, validation_df=df_test, freq=\"5min\")  # , early_stopping=True)\n",
    "# metrics = m.fit(df_train, validation_df=df_test, freq=\"5min\", progress='plot')  # , early_stopping=True)\n",
    "\n",
    "end = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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         "line": {
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          "width": 2
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         "mode": "lines",
         "name": "MAE",
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       "{'MAE_val': 0.6386953592300415,\n",
       " 'RMSE_val': 0.870656430721283,\n",
       " 'Loss_val': 0.0004427633248269558,\n",
       " 'RegLoss_val': 0.0,\n",
       " 'epoch': 89,\n",
       " 'MAE': 0.8121793270111084,\n",
       " 'RMSE': 1.4527992010116577,\n",
       " 'Loss': 0.0009107455844059587,\n",
       " 'RegLoss': 0.0}"
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     "metadata": {},
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   "execution_count": 10,
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       "      <th></th>\n",
       "      <th>MAE_val</th>\n",
       "      <th>RMSE_val</th>\n",
       "      <th>Loss_val</th>\n",
       "      <th>RegLoss_val</th>\n",
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       "      <th>89</th>\n",
       "      <td>0.638695</td>\n",
       "      <td>0.870656</td>\n",
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       "      <td>89</td>\n",
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       "     MAE_val  RMSE_val  Loss_val  RegLoss_val  epoch       MAE      RMSE  \\\n",
       "89  0.638695  0.870656  0.000443          0.0     89  0.812179  1.452799   \n",
       "\n",
       "        Loss  RegLoss  \n",
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    "metrics.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency 5T corresponds to 99.995% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - 5min\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency 5T corresponds to 99.995% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - 5min\n",
      "WARNING - (NP.data.processing._handle_missing_data) - 12 missing values in column y were detected in total. \n",
      "INFO - (NP.data.processing._handle_missing_data) - 12 NaN values in column y were auto-imputed.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff29390e116b4fa3bc7f15bb3477c053",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 132it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
     ]
    }
   ],
   "source": [
    "forecast = m.predict(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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          "2017-06-30T11:15:00",
          "2017-06-30T13:05:00",
          "2017-06-30T14:40:00",
          "2017-06-30T15:20:00",
          "2017-06-30T17:40:00",
          "2017-06-30T18:30:00",
          "2017-06-30T20:50:00",
          "2017-06-30T21:50:00",
          "2017-07-01T00:00:00",
          "2017-07-01T01:35:00",
          "2017-07-01T03:05:00",
          "2017-07-01T04:35:00",
          "2017-07-01T05:35:00",
          "2017-07-01T07:20:00",
          "2017-07-01T09:30:00",
          "2017-07-01T10:45:00",
          "2017-07-01T12:30:00",
          "2017-07-01T14:05:00",
          "2017-07-01T14:50:00",
          "2017-07-01T17:05:00",
          "2017-07-01T18:45:00",
          "2017-07-01T20:10:00",
          "2017-07-01T21:25:00",
          "2017-07-01T22:50:00",
          "2017-07-02T00:55:00",
          "2017-07-02T01:45:00",
          "2017-07-02T03:20:00",
          "2017-07-02T04:55:00",
          "2017-07-02T06:20:00",
          "2017-07-02T08:20:00",
          "2017-07-02T09:55:00",
          "2017-07-02T11:50:00",
          "2017-07-02T13:30:00",
          "2017-07-02T14:55:00",
          "2017-07-02T15:45:00",
          "2017-07-02T18:05:00",
          "2017-07-02T19:40:00",
          "2017-07-02T21:10:00",
          "2017-07-02T22:50:00",
          "2017-07-03T00:25:00",
          "2017-07-03T01:50:00",
          "2017-07-03T03:25:00",
          "2017-07-03T05:00:00",
          "2017-07-03T06:20:00",
          "2017-07-03T07:50:00",
          "2017-07-03T09:30:00",
          "2017-07-03T10:20:00",
          "2017-07-03T12:35:00",
          "2017-07-03T14:25:00",
          "2017-07-03T15:05:00",
          "2017-07-03T17:30:00",
          "2017-07-03T19:05:00",
          "2017-07-03T20:20:00",
          "2017-07-03T21:30:00",
          "2017-07-03T23:00:00",
          "2017-07-04T00:35:00",
          "2017-07-04T02:00:00",
          "2017-07-04T03:35:00",
          "2017-07-04T05:40:00",
          "2017-07-04T06:50:00",
          "2017-07-04T09:00:00",
          "2017-07-04T09:55:00",
          "2017-07-04T11:40:00",
          "2017-07-04T13:00:00",
          "2017-07-04T14:35:00",
          "2017-07-04T16:05:00",
          "2017-07-04T17:45:00",
          "2017-07-04T19:10:00",
          "2017-07-04T20:30:00",
          "2017-07-04T22:25:00",
          "2017-07-05T00:00:00"
         ],
         "y": [
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          5.3,
          9.9,
          28.3,
          35.2,
          38.2,
          35.6,
          28.3,
          16.8,
          13,
          10.4,
          6.9,
          5.9,
          6.3,
          6.1,
          4.8,
          7.4,
          12.8,
          25.2,
          34.4,
          39.2,
          38.4,
          33.2,
          17.2,
          14,
          12.6,
          10.6,
          8.8,
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          8.3,
          6.2,
          6.8,
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          21.1,
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          11.4,
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          7.6,
          6,
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          33,
          44,
          48,
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          21.3,
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          10,
          10,
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          8.7,
          10.4,
          16.2,
          29.2,
          27.4,
          43.1,
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          10.3,
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          8.2,
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          4.9,
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          41.8,
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          13.6,
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          24,
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          15.6,
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          42.5,
          49.3,
          52.3,
          49.7,
          43.8,
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          16.6,
          15.2,
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          15,
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          22,
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          41.8,
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          11.8,
          12,
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          39.4,
          47.8,
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          19,
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          14.8,
          14.8,
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          32,
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          9.9,
          9,
          8,
          7,
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          28.5,
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          10.7,
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          38.5,
          44.1,
          50.8,
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          14.9,
          12.7,
          11.7,
          10.2,
          10,
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          48,
          49.6,
          46.8,
          34.5,
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          10.3,
          10.6,
          9,
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          19.9,
          39.3,
          47.2,
          50,
          46.3,
          38.3,
          25.2,
          17.3,
          14.4,
          14.5,
          12.6,
          11.2,
          11,
          10,
          12.7,
          14.9,
          35.4,
          44.6,
          49,
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          46,
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          24.7,
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          14.2,
          13.8,
          11.5,
          12,
          11,
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          14,
          23.7,
          37.6,
          46.3,
          49.7,
          47.7,
          41.4
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          180,
          192,
          204,
          216,
          228,
          240,
          252,
          264,
          276,
          288
         ],
         "title": {
          "text": "Hour of day"
         }
        },
        "xaxis4": {
         "anchor": "y4",
         "domain": [
          0,
          1
         ],
         "linewidth": 1.5,
         "mirror": true,
         "range": [
          -2,
          39
         ],
         "showline": true,
         "title": {
          "text": "AR lag number"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0.80625,
          1
         ],
         "linewidth": 1.5,
         "mirror": true,
         "rangemode": "normal",
         "showline": true,
         "title": {
          "text": "Trend"
         }
        },
        "yaxis2": {
         "anchor": "x2",
         "domain": [
          0.5375,
          0.73125
         ],
         "linewidth": 1.5,
         "mirror": true,
         "rangemode": "normal",
         "showline": true,
         "title": {
          "text": "Trend Rate Change"
         }
        },
        "yaxis3": {
         "anchor": "x3",
         "domain": [
          0.26875,
          0.4625
         ],
         "linewidth": 1.5,
         "mirror": true,
         "rangemode": "normal",
         "showline": true,
         "title": {
          "text": "Seasonality: daily"
         }
        },
        "yaxis4": {
         "anchor": "x4",
         "domain": [
          0,
          0.19375
         ],
         "linewidth": 1.5,
         "mirror": true,
         "rangemode": "normal",
         "showline": true,
         "title": {
          "text": "AR weight (12)-ahead"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_param = m.plot_parameters()\n",
    "fig_param.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "display_name": ".venv",
   "language": "python",
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